PROJECT SUMMARY The emergence and global expansion of SARS-CoV-2 as a human pathogen over the last four months represents a nearly unprecedented challenge for the infectious disease modelling community. This pandemic has benefitted from huge volumes of data being generated, but the rate of dissemination of these data has often outpaced existing data pipelines. While the last decade has seen significant advances in real-time infectious disease forecasting ? spurred by rapid growth in data and computational methods ? these methods have primarily focused on seasonal endemic diseases based, are based on historical data, and so do not apply easily to this novel pathogen, or to pandemic scenarios. New methods are needed to leverage the wealth of surveillance data at fine spatial granularity, together with associated information about policy interventions and environmental conditions over space and time, to reason directly about the mechanisms to forecast and understand the transmission dynamics of SARS-CoV-2 transmission. These methods must use sound statistical and epidemiological principles and be flexible and computationally efficient to provide real- time forecasts to guide public health decision-making and respond to changing aspects of this global crisis. The central research activities of this project are (1) to develop scalable, computationally efficient Bayesian hierarchical compartmental models to flexibly respond to state-level public health forecasting needs, and (2) to design models and conduct analyses to draw robust inference about the effectiveness of interventions in impacting the reproductive rate of SARS-CoV-2 infections within the US to build an evidence-base for continued responses to COVID-19 and future pandemics.